Research on carbon emission prediction and economic policy based on TCN-LSTM combined with attention mechanism
文献类型：期刊论文
| 作者 | Wei, Xiaoyan1; Xu, Ying2,3 |
| 刊名 | FRONTIERS IN ECOLOGY AND EVOLUTION
 |
| 出版日期 | 2023-10-13 |
| 卷号 | 11页码:18 |
| 关键词 | LSTM TCN attention mechanism carbon emission prediction environmental issues |
| ISSN号 | 2296-701X |
| DOI | 10.3389/fevo.2023.1270248 |
| 通讯作者 | Wei, Xiaoyan(weixiaoyan201903@163.com) |
| 英文摘要 | IntroductionIn the face of increasingly severe global climate change and environmental challenges, reducing carbon emissions has become a key global priority. Deep learning, as a powerful artificial intelligence technology, has demonstrated significant capabilities in time series analysis and pattern recognition, opening up new avenues for carbon emission prediction and policy development.MethodsIn this study, we carefully collected and pre-processed four datasets to ensure the reliability and consistency of the data. Our proposed TCN-LSTM combination architecture effectively leverages the parallel computing capabilities of TCN and the memory capacity of LSTM, more efficiently capturing long-term dependencies in time series data. Furthermore, the introduction of an attention mechanism allows us to weigh important factors in historical data, thereby improving the accuracy and robustness of predictions.ResultsOur research findings provide novel insights and methods for advancing carbon emission prediction. Additionally, our discoveries offer valuable references for decision-makers and government agencies in formulating scientifically effective carbon reduction policies. As the urgency of addressing climate change continues to grow, the progress made in this paper can contribute to a more sustainable and environmentally conscious future.DiscussionIn this paper, we emphasize the potential of deep learning techniques in carbon emission prediction and demonstrate the effectiveness of the TCN-LSTM combination architecture. The significant contribution of this research lies in providing a new approach to address the carbon emission prediction problem in time series data. Moreover, our study underscores the importance of data reliability and consistency for the successful application of models. We encourage further research and application of this method to facilitate the achievement of global carbon reduction goals. |
| WOS关键词 | ENERGY-CONSUMPTION ; COUNTRIES ; NETWORK ; ARIMA ; TRADE |
| 资助项目 | The authors declare that no financial support was received for the research, authorship, and/or publication of this article. |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001088504400001 |
| 出版者 | FRONTIERS MEDIA SA |
| 资助机构 | The authors declare that no financial support was received for the research, authorship, and/or publication of this article. |
| 源URL | [http://ir.giec.ac.cn/handle/344007/40057]  |
| 专题 | 中国科学院广州能源研究所 |
| 通讯作者 | Wei, Xiaoyan |
| 作者单位 | 1.Univ Sci & Technol Liaoning, Sch Econ & Law, Anshan, Peoples R China 2.Jimei Univ, Coll Mech Equipment & Mech Engn, Xiamen, Peoples R China 3.Chinese Acad Sci, Guangzhou Inst Energy Convers, Guangzhou, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wei, Xiaoyan,Xu, Ying. Research on carbon emission prediction and economic policy based on TCN-LSTM combined with attention mechanism[J]. FRONTIERS IN ECOLOGY AND EVOLUTION,2023,11:18. |
| APA | Wei, Xiaoyan,&Xu, Ying.(2023).Research on carbon emission prediction and economic policy based on TCN-LSTM combined with attention mechanism.FRONTIERS IN ECOLOGY AND EVOLUTION,11,18. |
| MLA | Wei, Xiaoyan,et al."Research on carbon emission prediction and economic policy based on TCN-LSTM combined with attention mechanism".FRONTIERS IN ECOLOGY AND EVOLUTION 11(2023):18. |
入库方式： OAI收割
来源：广州能源研究所
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